SSLfmm: Semi-Supervised Learning under a Mixed-Missingness Mechanism in
Finite Mixture Models
Implements a semi-supervised learning framework for finite mixture
models under a mixed-missingness mechanism. The approach models both
missing completely at random (MCAR) and entropy-based missing at random
(MAR) processes using a logistic–entropy formulation. Estimation is carried
out via an Expectation–-Conditional Maximisation (ECM) algorithm with robust
initialisation routines for stable convergence. The methodology relates to
the statistical perspective and informative missingness behaviour discussed
in Ahfock and McLachlan (2020) <doi:10.1007/s11222-020-09971-5> and
Ahfock and McLachlan (2023) <doi:10.1016/j.ecosta.2022.03.007>. The package
provides functions for data simulation, model estimation, prediction, and
theoretical Bayes error evaluation for analysing partially labelled data
under a mixed-missingness mechanism.
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[aut,
cre],
Geoffrey J. McLachlan